{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 预备知识"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4.1 导数和微分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from IPython import display\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return 3 * x ** 2 - 4 * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numerical_lim(f, x, h):\n",
    "    return (f(x + h) - f(x)) / h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h= 0.10000, numerical limit= 2.30000\n",
      "h= 0.01000, numerical limit= 2.03000\n",
      "h= 0.00100, numerical limit= 2.00300\n",
      "h= 0.00010, numerical limit= 2.00030\n",
      "h= 0.00001, numerical limit= 2.00003\n"
     ]
    }
   ],
   "source": [
    "h = 0.1\n",
    "for i in range(5):\n",
    "    print(f'h={h: .5f}, numerical limit={numerical_lim(f, 1, h): .5f}')\n",
    "    h *= 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5.1 自动求导"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0., 1., 2., 3.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "x = torch.arange(4.0)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.requires_grad_(True)\n",
    "x.grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(28., grad_fn=<MulBackward0>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = 2 * torch.dot(x, x)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0.,  4.,  8., 12.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.backward()\n",
    "x.grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([True, True, True, True])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.grad == 4 * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0., 0., 0., 0.])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.grad.zero_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 1., 1., 1.])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = x.sum()\n",
    "y.backward()\n",
    "x.grad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6.1 基本概率论"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import torch\n",
    "from torch.distributions import multinomial\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0., 0., 1., 0., 0., 0.])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fair_probs = torch.ones([6]) / 6\n",
    "multinomial.Multinomial(1, fair_probs).sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([2., 3., 1., 1., 2., 1.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multinomial.Multinomial(10, fair_probs).sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.1620, 0.1680, 0.1690, 0.1650, 0.1700, 0.1660])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将结果存储为32位浮点数以进行除法\n",
    "counts = multinomial.Multinomial(1000, fair_probs).sample()\n",
    "counts / 1000    # 相对频率作为估算值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = multinomial.Multinomial(10, fair_probs).sample((500,))\n",
    "cum_counts = counts.cumsum(dim=0)\n",
    "estimates = cum_counts / cum_counts.sum(dim=1, keepdims=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xaa81e7b0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "for i in range(6):\n",
    "    plt.plot(estimates[:, i].numpy(), \n",
    "             label=(\"P(die=\" + str(i+1) + \")\") )\n",
    "plt.axhline(y=0.167, color='black', linestyle='dashed')\n",
    "plt.xlabel('Experiment times')    # 实验次数\n",
    "plt.ylabel('Estimated prob')    # 估算概率\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
